Field of the Invention
This invention relates in general to the field energy consumption, and more particularly to an apparatus and method for automated metering data validation, estimation, and editing, and applications thereof.
Description of the Related Art
One problem with resources such as electricity, water, fossil fuels, and their derivatives (e.g., natural gas) is related to supply and demand. That is, production of a resource often is not in synchronization with demand for the resource. In addition, the delivery and transport infrastructure for these resources is limited in that it cannot instantaneously match production levels to provide for constantly fluctuating consumption levels. As anyone who has participated in a rolling blackout will concur, the times are more and more frequent when resource consumers are forced to face the realities of limited resource production.
Another problem with resources such as water and fossil fuels (which are ubiquitously employed to produce electricity) is their limited supply along with the detrimental impacts (e.g., carbon emissions) of their use. Public and political pressure for conservation of resources is prevalent, and the effects of this pressure is experienced across the spectrum of resource providers, resource producers and managers, and resource consumers.
It is no surprise, then, that the electrical power generation and distribution community has been taking proactive measures to protect limited instantaneous supplies of electrical power by 1) imposing demand charges on consumers in addition to their monthly usage charge and 2) providing incentives for conservation in the form of rebates and reduced charges. In prior years, consumers merely paid for the total amount of power that they consumed over a billing period. Today, most energy suppliers are not only charging customers for the total amount of electricity they have consumed over the billing period, but they are additionally imposing time of use charges and charging for peak demand. Peak demand is the greatest amount of energy that a customer uses during a measured period of time, typically on the order of minutes. Time of use charges fluctuate throughout the day to dissuade customers from using energy during peak consumption hours. Moreover, energy suppliers are providing rebate and incentive programs that reward consumers for so called energy efficiency upgrades (e.g., lighting and surrounding environment intelligently controlled, efficient cooling and refrigeration, etc.) in their facilities that result in reductions of both peak consumption, time of use consumption shifting, and overall energy consumption. Similar programs are prevalent in the water production and consumption community as well. It is anticipated that such programs will extend to other limited supply energy sources, such as, but not limited to, natural gas, nuclear energy, and other fuel sources.
Demand reduction and energy efficiency programs may be implemented and administered directly by energy providers (i.e., the utilities themselves) or they may be contracted out to third parties, so called energy services companies (ESCOs). ESCOs directly contract with energy consumers and also contract with the energy providers to, say, reduce the demand of a certain resource in a certain area by a specified percentage, where the reduction may be constrained to a certain period of time (i.e., via a demand response program). Or, the reduction effort may be ongoing (i.e., via an energy efficiency program).
The above scenarios are merely examples of the types of programs that are employed in the art to reduce consumption and foster conservation of limited resources. Regardless of the vehicle that is employed, what is important to both producers and consumers is that they be able to understand and appreciate the effects of demand reduction and efficiency improvements that are performed. Sometimes the understanding and appreciation can occur hours, days, or even weeks after the consumption occurs. But the present inventors have observed that there is increasing desire in the art for such understanding and appreciation to occur minutes after the consumption occurs, such as in real time and near real time systems. This disclosure is provided to solve problems that are present in real time and near real time systems.
As one skilled in the art will appreciate, many real time and near real time systems perform operations based on energy consumption data provided by streaming energy consumption metering sources such as smart meters and building automation system meters that measure the energy consumed by one or more corresponding devices and periodically transmit measurements over a conventional wired or wireless network. One skilled will also appreciate that the measurements that are transported over the networks may interspersed with intermittent errors due to power outages, device or metering source malfunctions, weather conditions affecting transmission, network problems, etc.
In fact, streaming data quality and accuracy problems are so common that standardized processes are being developed that set requirements for validation, estimation, and editing (VEE) of metered energy consumption data. These standards address such things as power outages, missing interval values, atypical interval values, suspect groups of interval values, time stamping, reactive energy issues, and a significant number of estimation methods to be employed for particular types of detected anomalies, specific types of meters, and certain known scenarios. Thus, received data must be first validated, that is, examined and tagged either as valid data or anomalous data. Next, estimation techniques are employed to generate estimates for the anomalous data. Finally, editing occurs where the anomalous data is replaced with the estimates, resulting is what is known as post VEE data. The post VEE data is then employed by components within the real time and near real time systems to perform their respective operations and control functions.
Because these systems perform their operations and functions in real time or near real time, the processes that perform VEE are time constrained to provide post VEE data. And these systems attempt to balance processing capacity (and ultimately, cost) with post VEE data accuracy, typically as a function of number of streaming sources processed and the time allocated for VEE processing.
The present inventors have noted that virtually all real time and near real time systems that process a substantial number of energy consumption streams (say, between 50 and 50,000 streams) employ so-called “lightweight” VEE in order to achieve required throughput. As one skilled will appreciate, many different techniques exist to detect and correct anomalous data, where more accurate post VEE data can be had by using techniques that are well suited for individual stream types and anomaly durations. But to tailor VEE techniques for each stream in a system that processes a substantial number of streams would require an inordinate amount of data analyst time and effort. Consequently, lightweight VEE is a compromise, typically employing a small number of VEE techniques for all streams processed by a system. Throughput is achieved, data analyst time is controlled, and post VEE data is accepted as being as accurate as can be had.
The present inventors have also noted, though, that there is increasing desire in the art for more accurate post VEE data for use by real time and near real time systems, over that which is presently available. Accordingly, what is needed is an apparatus and method for automatically configuring VEE techniques in real time and near real time systems.
What is also needed is an automated validation, estimation, and editing processor that configures and dynamically optimizes VEE technique for individual data streams.
What is additionally needed is a network operations center that automatically configures and dynamically optimizes validation, estimation, and editing techniques corresponding to a plurality of energy consumption data streams.
What is furthermore needed is an energy baselining system that automatically configures and dynamically optimizes validation, estimation, and editing techniques corresponding to a plurality of energy consumption data streams.
What is moreover needed is a demand response prediction system that automatically configures and dynamically optimizes validation, estimation, and editing techniques corresponding to a plurality of energy consumption data streams.
What is furthermore needed is a brown out prediction system that automatically configures and dynamically optimizes validation, estimation, and editing techniques corresponding to a plurality of energy consumption data streams.
What is yet additionally needed is a building control system that automatically configures and dynamically optimizes validation, estimation, and editing techniques corresponding to a plurality of energy consumption data streams.